Data-driven recognition of self-dual binary symbols on curved and reflective surfaces

A robust and effective two-dimensional symbol code is developed that is decodable in a bottom-up fashion imposing minimal constraints from a priori models. For binary encoding of arbitrary data two equally sized, lattice-like arranged, quadratic symbols are proposed which only differ in their complementary brightness distribution. For symbol code recognition the orientation parameter of the symbols is estimated from their own higher level texture. Then, normalized matching with a single correlation filter concurrently detects both symbol types, whereby dependently the response is either strongly positive or negative. The filter output is the correlation score whose local maxima are adaptively extracted by morphological dilation. Finally, a fuzzy region-merging approach based on four-neighborhoods restores the encoded bit matrix.